Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method for selecting an optimal date for a planned event using machine-based natural language processing, the computer-implemented method comprising: receiving, by a data processing system, event input data that includes at least one of a location for a planned event, a range of dates for the planned event, attributes of a target audience for the planned event, and attributes of the planned event; retrieving, by the data processing system, web search data from at least one network and social media data corresponding to the received event input data from at least one website; analyzing, by the data processing system, the received event input data and the retrieved web search data and social media data that correspond to the received event input data using the machine-based natural language processing to identify a set of optimal dates to maximize attendance of the target audience at the planned event; ranking, by the data processing system, the identified set of optimal dates for the planned event based on weights assigned to each of the attributes of the target audience and the attributes of the planned event; selecting, by the data processing system, a highest-ranking date in the identified set of optimal dates for the planned event; and performing, by the data processing system, a set of action steps pertaining to the planned event based on the selected highest-ranking date for the planned event.
This invention relates to a computer-implemented method for selecting an optimal date for a planned event using machine-based natural language processing. The method addresses the challenge of determining the best date for an event to maximize attendance by analyzing various factors such as location, date range, target audience attributes, and event attributes. The system receives input data including these factors and retrieves relevant web search data and social media data from online sources. Using natural language processing, the system analyzes the input data and retrieved information to identify a set of optimal dates that are likely to attract the highest attendance. The identified dates are then ranked based on weighted attributes of the target audience and the event itself. The highest-ranking date is selected, and the system performs action steps related to the event, such as scheduling or notifications, based on this selection. This approach leverages machine learning and data analysis to optimize event planning by considering real-time online data and audience preferences.
2. The computer-implemented method of claim 1 further comprising: assigning, by the data processing system, different weights to each of the attributes of the target audience and the attributes of the planned event responsive to determining that the highest-ranking date for the planned event is not successful based on a defined threshold level of success.
This invention relates to optimizing event scheduling by analyzing audience attributes and event attributes to determine the best date for an event. The method involves collecting data on a target audience, including demographic, behavioral, and preference attributes, and data on a planned event, including logistical, thematic, and promotional attributes. The system evaluates the compatibility between audience attributes and event attributes to rank potential event dates based on predicted success. If the highest-ranking date does not meet a predefined success threshold, the system adjusts the weighting of audience and event attributes to refine the ranking. This iterative process ensures that the selected date maximizes audience engagement and event success by dynamically balancing the influence of different factors. The method leverages machine learning or statistical models to analyze the data and assign weights to attributes, improving the accuracy of date selection over time. The goal is to enhance event planning efficiency by reducing trial-and-error scheduling and increasing the likelihood of successful audience turnout.
3. The computer-implemented method of claim 1 , wherein the set of action steps includes scheduling the planned event on the selected highest-ranking date, reserving a site for the planned event, and sending event invitations to the target audience.
This invention relates to automated event planning systems that optimize scheduling based on participant availability and other constraints. The method addresses the challenge of efficiently organizing events by analyzing input data to determine the best possible date and time for an event, then executing a series of automated actions to finalize the event details. The system processes input data, including participant availability, venue constraints, and other relevant factors, to generate a ranked list of potential dates for the planned event. The highest-ranking date is selected based on predefined criteria, such as maximum participant availability or venue suitability. Once the optimal date is identified, the system automatically schedules the event, reserves an appropriate site, and sends invitations to the target audience. The automated actions ensure that the event planning process is streamlined, reducing manual effort and improving efficiency. The method may also include additional steps, such as confirming participant responses, adjusting reservations based on attendance, and providing reminders to attendees. The system dynamically adapts to changes in availability or constraints, ensuring that the event remains well-organized and optimized throughout the planning process. This approach enhances productivity for event organizers while minimizing scheduling conflicts and logistical challenges.
4. The computer-implemented method of claim 1 further comprising: receiving, by the data processing system, post-event data corresponding to the planned event; and applying, by the data processing system, cognitive machine learning to the post-event data to increase event date selection accuracy for future events.
This invention relates to event planning systems that use machine learning to improve event date selection accuracy. The system addresses the challenge of selecting optimal event dates by analyzing historical and post-event data to refine future date recommendations. The method involves collecting data related to planned events, including factors such as attendance, engagement metrics, and external conditions like weather or competitor events. After the event occurs, the system gathers post-event data, such as actual attendance figures, participant feedback, and other performance indicators. Cognitive machine learning techniques are then applied to this post-event data to identify patterns and correlations that influence event success. The learned insights are used to enhance the accuracy of future event date recommendations, ensuring better alignment with factors that maximize attendance and engagement. The system continuously improves its predictive capabilities by iteratively refining its models with new data, leading to more informed and effective event planning decisions. This approach reduces reliance on manual analysis and guesswork, optimizing resource allocation and increasing the likelihood of successful events.
5. The computer-implemented method of claim 1 , wherein the attributes of the target audience include age range of potential attendees, income of potential attendees, geographic location of potential attendees, and personal interests and preferences of potential attendees that match keywords describing a focus of the planned event.
This invention relates to a computer-implemented method for optimizing event planning by analyzing target audience attributes to improve event promotion and attendance. The method addresses the challenge of effectively reaching potential attendees by leveraging detailed demographic and behavioral data to tailor event marketing strategies. The method involves collecting and processing data on potential attendees, including their age range, income level, geographic location, and personal interests. These attributes are matched against keywords describing the event's focus to identify the most relevant audience segments. The system then uses this data to generate targeted promotional materials, such as advertisements or invitations, designed to maximize engagement and attendance. Additionally, the method may include analyzing historical event data to refine audience targeting further. By comparing past event success metrics with current audience attributes, the system can predict which segments are most likely to attend and adjust promotional efforts accordingly. This approach ensures that event organizers can allocate resources efficiently and reach the most receptive audience. The invention enhances event planning by providing data-driven insights that improve outreach effectiveness, ultimately increasing attendance and event success.
6. The computer-implemented method of claim 1 , wherein the attributes of the planned event include type of event, cost of attendance, and one or more keywords describing a focus of the planned event.
This invention relates to a computer-implemented method for managing event planning, specifically addressing the need to efficiently organize and categorize planned events based on key attributes. The method involves storing and processing event data to enhance event management systems by incorporating detailed event attributes. These attributes include the type of event, the cost of attendance, and one or more keywords that describe the event's focus. By capturing these attributes, the system enables better event categorization, filtering, and retrieval, allowing users to search and compare events more effectively. The inclusion of event type helps distinguish between different categories such as conferences, workshops, or social gatherings, while the cost attribute allows for budget-based filtering. Keywords describing the event's focus provide contextual relevance, improving search accuracy and personalization. This structured approach ensures that event planners and attendees can access relevant information quickly, optimizing event planning and participation. The method may be integrated into broader event management platforms, enhancing their functionality by providing a more granular and organized way to handle event data.
7. The computer-implemented method of claim 1 , wherein the web search data include information regarding same or similar events already scheduled in or near a location for the planned event on or near the identified set of optimal dates for the planned event, information regarding businesses, entertainment, and places of interest near the planned event, information regarding construction projects near the planned event, and information regarding city events already scheduled on the identified set of optimal dates for the planned event.
This invention relates to a computer-implemented method for optimizing event planning by analyzing web search data to identify potential conflicts or opportunities related to a planned event. The method involves gathering and processing web search data to determine the best dates for an event by assessing factors such as existing scheduled events, nearby businesses, entertainment venues, points of interest, construction projects, and city-wide events. By analyzing these data points, the system helps event planners avoid scheduling conflicts, identify high-traffic areas, and consider external factors that could impact attendance or logistics. The method ensures that the selected dates minimize disruptions from competing events, construction, or other local activities, while also leveraging nearby attractions to enhance the event experience. This approach improves decision-making by providing a comprehensive view of the event's environment, ensuring optimal timing and location selection. The system dynamically adjusts recommendations based on real-time data, allowing for more informed and strategic event planning.
8. The computer-implemented method of claim 1 , wherein the social media data include information regarding social media users that have personal interests or preferences that match information in the attributes of the target audience, information regarding social media users that have previously attended events that are same or similar to the planned event, information regarding social media users that are available during the identified set of optimal dates for the planned event.
This invention relates to a computer-implemented method for identifying and selecting social media users to promote a planned event. The method addresses the challenge of efficiently targeting potential attendees by leveraging social media data to find users whose interests, past event attendance, and availability align with the event's requirements. The system analyzes social media data to identify users with personal interests or preferences matching the target audience's attributes, such as hobbies, demographics, or professional backgrounds. It also identifies users who have previously attended similar events, indicating a higher likelihood of interest. Additionally, the method filters users based on their availability during the optimal dates for the planned event, ensuring that the selected audience is not only relevant but also likely to attend. By combining these criteria, the system enhances the precision of event promotion, increasing the chances of successful attendance and engagement. The method automates the selection process, reducing manual effort and improving efficiency in event marketing.
9. A data processing system for selecting an optimal date for a planned event using machine-based natural language processing, the data processing system comprising: a bus system; a storage device connected to the bus system, wherein the storage device stores program instructions; and a processor connected to the bus system, wherein the processor executes the program instructions to: receive event input data that includes at least one of a location for a planned event, a range of dates for the planned event, attributes of a target audience for the planned event, and attributes of the planned event; retrieve web search data from at least one network and social media data corresponding to the received event input data from at least one web site; analyze the received event input data and the retrieved web search data and social media data that correspond to the received event input data using the machine-based natural language processing to identify a set of optimal dates to maximize attendance of the target audience at the planned event; rank the identified set of optimal dates for the planned event based on weights assigned to each of the attributes of the target audience and the attributes of the planned event; select a highest-ranking date in the identified set of optimal dates for the planned event; and perform a set of action steps pertaining to the planned event based on the selected highest-ranking date for the planned event.
The invention relates to a data processing system that uses machine-based natural language processing to determine the optimal date for a planned event by analyzing web and social media data. The system addresses the challenge of selecting event dates that maximize attendance by leveraging digital data sources to assess audience preferences and event attributes. The system includes a processor, storage device, and bus system. It receives input data about the event, such as location, date range, target audience attributes, and event attributes. Using this input, the system retrieves relevant web search data and social media data from online sources. Machine-based natural language processing analyzes the collected data to identify optimal dates that align with audience availability and event suitability. The system ranks these dates based on weighted attributes of the target audience and event characteristics, then selects the highest-ranking date. Finally, it performs action steps related to the event, such as scheduling or notifications, based on the chosen date. This approach ensures data-driven event planning to enhance attendance and engagement.
10. The data processing system of claim 9 , wherein the processor further executes the program instructions to: assign different weights to each of the attributes of the target audience and the attributes of the planned event responsive to determining that the highest-ranking date for the planned event is not successful based on a defined threshold level of success.
This invention relates to a data processing system for optimizing event scheduling by analyzing audience attributes and event attributes to determine the best date for an event. The system addresses the challenge of selecting an optimal event date by evaluating multiple factors, including audience availability, preferences, and event logistics, to maximize attendance and engagement. The system processes data representing attributes of a target audience, such as demographics, schedules, and past behavior, and attributes of a planned event, such as location, duration, and type. It ranks potential event dates based on predicted success metrics, such as attendance likelihood or engagement levels. If the highest-ranking date does not meet a predefined success threshold, the system adjusts the analysis by assigning different weights to the audience and event attributes. This reweighting refines the ranking process to improve the likelihood of selecting a more successful date. The system may also consider external factors, such as competing events or seasonal trends, to further enhance date selection accuracy. The goal is to dynamically adapt the evaluation criteria to ensure the chosen date aligns with both audience preferences and event objectives.
11. The data processing system of claim 9 , wherein the set of action steps includes scheduling the planned event on the selected highest-ranking date, reserving a site for the planned event, and sending event invitations to the target audience.
This invention relates to a data processing system for managing and executing planned events, particularly in scenarios where multiple potential dates and locations must be evaluated to optimize event success. The system addresses the challenge of efficiently selecting the best date and site for an event by analyzing various factors such as audience availability, venue capacity, and logistical constraints. The system generates a ranked list of potential dates and sites based on predefined criteria, then executes a series of action steps to finalize the event. These steps include scheduling the event on the highest-ranking date, reserving the selected site, and distributing invitations to the target audience. The system automates the decision-making process, reducing manual effort and improving event planning efficiency. The invention ensures that events are scheduled at optimal times and locations, maximizing attendance and resource utilization. The system may also integrate with external databases or APIs to gather real-time data on venue availability, audience preferences, and other relevant factors. By streamlining the event planning workflow, the invention enhances productivity for organizers and improves the overall event experience for participants.
12. The data processing system of claim 9 , wherein the processor further executes the program instructions to: receive post-event data corresponding to the planned event; and apply cognitive machine learning to the post-event data to increase event date selection accuracy for future events.
The invention relates to a data processing system for optimizing event date selection using machine learning. The system addresses the challenge of accurately predicting optimal dates for events by leveraging historical and post-event data to refine future predictions. The system includes a processor that executes program instructions to collect and analyze data related to planned events, such as attendance, engagement metrics, and external factors like weather or holidays. After an event occurs, the system receives post-event data, which may include actual attendance figures, participant feedback, or other performance indicators. Using cognitive machine learning techniques, the system processes this post-event data to identify patterns and correlations that improve the accuracy of future event date selections. The machine learning model continuously updates based on new data, enhancing its predictive capabilities over time. This iterative learning process ensures that the system adapts to changing conditions and user preferences, providing more reliable event scheduling recommendations. The system may also integrate with external data sources to further refine its predictions, such as social media trends or economic indicators. By applying advanced analytics to both pre- and post-event data, the invention enables organizations to make data-driven decisions for event planning, ultimately improving attendance and engagement outcomes.
13. A computer program product for selecting an optimal date for a planned event using machine-based natural language processing, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a data processing system to cause the data processing system to perform a method comprising: receiving, by the data processing system, event input data that includes at least one of a location for a planned event, a range of dates for the planned event, attributes of a target audience for the planned event, and attributes of the planned event; retrieving, by the data processing system, web search data from at least one network and social media data corresponding to the received event input data from at least one website; analyzing, by the data processing system, the received event input data and the retrieved web search data and social media data that correspond to the received event input data using the machine-based natural language processing to identify a set of optimal dates to maximize attendance of the target audience at the planned event; ranking, by the data processing system, the identified set of optimal dates for the planned event based on weights assigned to each of the attributes of the target audience and the attributes of the planned event; selecting, by the data processing system, a highest-ranking date in the identified set of optimal dates for the planned event; and performing, by the data processing system, a set of action steps pertaining to the planned event based on the selected highest-ranking date for the planned event.
This invention relates to event planning and optimization using machine-based natural language processing (NLP) to determine the best date for an event. The system helps organizers maximize attendance by analyzing various factors such as location, date range, target audience attributes, and event characteristics. It collects web search data and social media data relevant to the event details, then processes this information using NLP to identify optimal dates. The system ranks these dates based on weighted attributes of the audience and event, selecting the highest-ranking date. Once selected, the system performs actions related to the event, such as scheduling or notifications. The solution addresses the challenge of choosing event dates that align with audience availability and preferences, improving attendance and engagement. By leveraging machine learning and NLP, the system automates the decision-making process, reducing manual effort and enhancing event planning efficiency.
14. The computer program product of claim 13 further comprising: assigning, by the data processing system, different weights to each of the attributes of the target audience and the attributes of the planned event responsive to determining that the highest-ranking date for the planned event is not successful based on a defined threshold level of success.
This invention relates to optimizing event scheduling by analyzing audience attributes and event attributes to determine the best date for an event. The system identifies a target audience with specific attributes and a planned event with its own set of attributes. Initially, the system ranks potential event dates based on compatibility between the audience and event attributes. If the highest-ranking date does not meet a predefined success threshold, the system adjusts the weighting of the attributes to improve the ranking process. This dynamic weighting allows the system to refine its selection criteria and propose a more suitable date. The method involves processing data to calculate compatibility scores, comparing these scores to success thresholds, and iteratively adjusting attribute weights to enhance scheduling accuracy. The invention aims to improve event planning by ensuring that the chosen date aligns better with audience preferences and event requirements, thereby increasing the likelihood of success. The system may also consider additional factors such as venue availability or external constraints to further refine the scheduling decision.
15. The computer program product of claim 13 , wherein the set of action steps includes scheduling the planned event on the selected highest-ranking date, reserving a site for the planned event, and sending event invitations to the target audience.
This invention relates to automated event planning and scheduling systems. The problem addressed is the inefficiency and complexity of manually planning events, including selecting optimal dates, reserving venues, and coordinating invitations. The solution involves a computer program product that automates these tasks by analyzing multiple factors to determine the best date for an event, such as availability, audience preferences, and venue constraints. The system generates a ranked list of potential dates based on these factors and then executes a set of action steps to finalize the event. These steps include scheduling the event on the highest-ranking date, reserving a suitable site or venue, and sending invitations to the target audience. The program may also integrate with calendar systems, venue booking platforms, and communication tools to streamline the process. By automating these tasks, the system reduces manual effort, minimizes scheduling conflicts, and improves event planning efficiency. The invention is particularly useful for organizations or individuals who frequently organize events and need a systematic way to handle logistics.
16. The computer program product of claim 13 further comprising: receiving, by the data processing system, post-event data corresponding to the planned event; and applying, by the data processing system, cognitive machine learning to the post-event data to increase event date selection accuracy for future events.
This invention relates to event planning systems that use machine learning to improve event date selection accuracy. The system collects and analyzes historical event data, including factors like attendance, weather, and participant feedback, to predict optimal dates for future events. The system also receives post-event data after an event occurs, such as actual attendance numbers, participant feedback, and external conditions like weather or local events. Using cognitive machine learning techniques, the system processes this post-event data to refine its predictive models, improving the accuracy of future event date recommendations. The machine learning models may incorporate various data sources, including user preferences, historical trends, and external data feeds, to generate optimized event scheduling suggestions. The system aims to enhance event planning efficiency by reducing the risk of poor attendance or logistical issues due to suboptimal date selection. The invention is particularly useful for organizations that frequently host events and need data-driven insights to maximize participation and success.
17. The computer program product of claim 13 , wherein the attributes of the target audience include age range of potential attendees, income of potential attendees, geographic location of potential attendees, and personal interests and preferences of potential attendees that match keywords describing a focus of the planned event.
This invention relates to a computer program product for optimizing event planning by analyzing and targeting specific audience attributes. The system identifies and selects potential attendees for an event based on demographic and behavioral data, including age range, income level, geographic location, and personal interests that align with the event's focus. The program uses keyword matching to determine relevance between audience preferences and event descriptions, ensuring targeted outreach. Additionally, the system may analyze historical event data to refine audience selection criteria, improving the accuracy of attendee predictions. The program can also generate recommendations for event details, such as timing, location, and promotional strategies, based on the identified audience attributes. By leveraging this data-driven approach, the system enhances event planning efficiency and increases the likelihood of successful attendance. The invention is particularly useful for organizers seeking to maximize engagement and attendance by precisely targeting the most relevant audience segments.
18. The computer program product of claim 13 , wherein the attributes of the planned event include type of event, cost of attendance, and one or more keywords describing a focus of the planned event.
This invention relates to a computer program product for managing and analyzing planned events, particularly in the context of scheduling, cost assessment, and categorization. The system addresses the challenge of efficiently organizing and retrieving event information by associating detailed attributes with each planned event. These attributes include the type of event (e.g., conference, workshop, seminar), the cost of attendance, and one or more keywords that describe the event's focus or subject matter. By storing these attributes, the system enables users to filter, search, and compare events based on specific criteria, improving decision-making and resource allocation. The program product may integrate with existing calendar or event management tools, allowing users to input or retrieve event details programmatically. The inclusion of cost and keyword attributes enhances the system's utility for budgeting and thematic analysis, ensuring that users can quickly identify relevant events that align with their interests or financial constraints. This structured approach to event data management streamlines planning processes and supports automated event recommendations based on user preferences or organizational priorities.
19. The computer program product of claim 13 , wherein the web search data include information regarding same or similar events already scheduled in or near a location for the planned event on or near the identified set of optimal dates for the planned event, information regarding businesses, entertainment, and places of interest near the planned event, information regarding construction projects near the planned event, and information regarding city events already scheduled on the identified set of optimal dates for the planned event.
This invention relates to a computer program product for optimizing event planning by analyzing web search data to identify optimal dates for a planned event. The system addresses the challenge of selecting event dates that minimize conflicts with other local activities, disruptions, and competing attractions, thereby improving attendance and overall event success. The program collects and processes web search data to determine the best dates for an event by evaluating factors such as existing scheduled events, nearby businesses, entertainment venues, points of interest, construction projects, and city-wide events. By analyzing these data points, the system identifies dates with minimal conflicts, ensuring the planned event does not overlap with major local activities that could reduce participation. The solution enhances event planning by leveraging real-time and historical web data to provide actionable insights, helping organizers avoid scheduling conflicts and maximize attendance. The system integrates multiple data sources to provide a comprehensive assessment of potential event dates, ensuring planners can make informed decisions. This approach improves event logistics by considering external factors that could impact attendance, such as competing events or construction-related disruptions. The invention is particularly useful for large-scale events where timing and location are critical to success.
20. The computer program product of claim 13 , wherein the social media data include information regarding social media users that have personal interests or preferences that match information in the attributes of the target audience, information regarding social media users that have previously attended events that are same or similar to the planned event, information regarding social media users that are available during the identified set of optimal dates for the planned event.
This invention relates to a system for optimizing event planning by analyzing social media data to identify an optimal target audience and event dates. The problem addressed is the difficulty in determining the best audience and timing for events based on real-world user behavior and preferences. The system collects and processes social media data to identify users whose personal interests or preferences align with the attributes of the target audience for a planned event. It also identifies users who have previously attended similar events and those who are available during the optimal dates for the event. By analyzing this data, the system helps event planners select the most relevant audience and the best timing for the event, increasing the likelihood of attendance and engagement. The invention improves upon traditional event planning methods by leveraging social media insights to make data-driven decisions, reducing guesswork and enhancing event success rates. The system may be implemented as a computer program product that processes and analyzes the social media data to provide actionable recommendations for event organizers.
Unknown
April 7, 2020
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